Overview

Dataset statistics

Number of variables25
Number of observations52792
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory35.8 MiB
Average record size in memory711.9 B

Variable types

Numeric16
Categorical9

Alerts

surface_composition has a high cardinality: 1088 distinct values High cardinality
ads_IE_1 is highly correlated with coverages and 9 other fieldsHigh correlation
ads_S_1 is highly correlated with coverages and 8 other fieldsHigh correlation
ads_IE_2 is highly correlated with coverages and 12 other fieldsHigh correlation
ads_H_2 is highly correlated with coverages and 15 other fieldsHigh correlation
ads_S_2 is highly correlated with coverages and 15 other fieldsHigh correlation
ads_IE_3 is highly correlated with coverages and 10 other fieldsHigh correlation
ads_H_3 is highly correlated with coverages and 11 other fieldsHigh correlation
ads_S_3 is highly correlated with coverages and 13 other fieldsHigh correlation
equation is highly correlated with coverages and 15 other fieldsHigh correlation
ads_1 is highly correlated with coverages and 10 other fieldsHigh correlation
ads_3 is highly correlated with coverages and 10 other fieldsHigh correlation
coverages is highly correlated with equation and 15 other fieldsHigh correlation
ads_2 is highly correlated with coverages and 15 other fieldsHigh correlation
site_1 is highly correlated with coverages and 14 other fieldsHigh correlation
site_2 is highly correlated with coverages and 13 other fieldsHigh correlation
site_3 is highly correlated with coverages and 11 other fieldsHigh correlation
efermi is highly correlated with site_1 and 2 other fieldsHigh correlation
formation_energy_per_atom is highly correlated with efermi and 1 other fieldsHigh correlation
volume is highly correlated with efermi and 1 other fieldsHigh correlation
ads_H_1 is highly correlated with coverages and 12 other fieldsHigh correlation
df_index has unique values Unique
band_gap has 52455 (99.4%) zeros Zeros
formation_energy_per_atom has 1824 (3.5%) zeros Zeros
ads_IE_2 has 27394 (51.9%) zeros Zeros
ads_H_2 has 27394 (51.9%) zeros Zeros
ads_S_2 has 27394 (51.9%) zeros Zeros
ads_IE_3 has 42593 (80.7%) zeros Zeros
ads_H_3 has 42593 (80.7%) zeros Zeros
ads_S_3 has 42593 (80.7%) zeros Zeros

Reproduction

Analysis started2022-10-11 17:48:24.946937
Analysis finished2022-10-11 17:49:22.264938
Duration57.32 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct52792
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44167.81077
Minimum0
Maximum88586
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size412.6 KiB
2022-10-11T12:49:22.406766image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3708.55
Q122110.75
median44317.5
Q366917.25
95-th percentile84224.45
Maximum88586
Range88586
Interquartile range (IQR)44806.5

Descriptive statistics

Standard deviation25685.26766
Coefficient of variation (CV)0.5815381659
Kurtosis-1.199111668
Mean44167.81077
Median Absolute Deviation (MAD)22369
Skewness-0.004307604397
Sum2331707066
Variance659732975
MonotonicityStrictly increasing
2022-10-11T12:49:22.558397image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
589801
 
< 0.1%
589701
 
< 0.1%
589711
 
< 0.1%
589721
 
< 0.1%
589731
 
< 0.1%
589741
 
< 0.1%
589751
 
< 0.1%
589761
 
< 0.1%
589771
 
< 0.1%
Other values (52782)52782
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
885861
< 0.1%
885851
< 0.1%
885791
< 0.1%
885781
< 0.1%
885771
< 0.1%
885761
< 0.1%
885751
< 0.1%
885741
< 0.1%
885691
< 0.1%
885681
< 0.1%

surface_composition
Categorical

HIGH CARDINALITY

Distinct1088
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
Pt3Ir
 
112
Au3Cu
 
111
Au3Pd
 
108
Ag3Pd
 
108
Ir3Rh
 
107
Other values (1083)
52246 

Length

Max length5
Median length5
Mean length4.460031823
Min length1

Characters and Unicode

Total characters235454
Distinct characters36
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowAg
2nd rowAg
3rd rowAg
4th rowAg
5th rowAg

Common Values

ValueCountFrequency (%)
Pt3Ir112
 
0.2%
Au3Cu111
 
0.2%
Au3Pd108
 
0.2%
Ag3Pd108
 
0.2%
Ir3Rh107
 
0.2%
Ag3Cu107
 
0.2%
Rh3Os106
 
0.2%
Pd3Cu105
 
0.2%
Pt3Rh104
 
0.2%
OsRu104
 
0.2%
Other values (1078)51720
98.0%

Length

2022-10-11T12:49:22.700649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pt3ir112
 
0.2%
au3cu111
 
0.2%
au3pd108
 
0.2%
ag3pd108
 
0.2%
ir3rh107
 
0.2%
ag3cu107
 
0.2%
rh3os106
 
0.2%
pd3cu105
 
0.2%
pd3au104
 
0.2%
pt3rh104
 
0.2%
Other values (1078)51720
98.0%

Most occurring characters

ValueCountFrequency (%)
333917
 
14.4%
P12196
 
5.2%
A11637
 
4.9%
u11010
 
4.7%
C10519
 
4.5%
n10511
 
4.5%
R10195
 
4.3%
r9038
 
3.8%
T8471
 
3.6%
a8117
 
3.4%
Other values (26)109843
46.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter103760
44.1%
Lowercase Letter97777
41.5%
Decimal Number33917
 
14.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P12196
11.8%
A11637
11.2%
C10519
10.1%
R10195
9.8%
T8471
 
8.2%
I7058
 
6.8%
S6312
 
6.1%
Z6007
 
5.8%
N5503
 
5.3%
H4114
 
4.0%
Other values (9)21748
21.0%
Lowercase Letter
ValueCountFrequency (%)
u11010
11.3%
n10511
10.7%
r9038
 
9.2%
a8117
 
8.3%
i7441
 
7.6%
d7240
 
7.4%
l5770
 
5.9%
g5517
 
5.6%
t5163
 
5.3%
e4848
 
5.0%
Other values (6)23122
23.6%
Decimal Number
ValueCountFrequency (%)
333917
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin201537
85.6%
Common33917
 
14.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
P12196
 
6.1%
A11637
 
5.8%
u11010
 
5.5%
C10519
 
5.2%
n10511
 
5.2%
R10195
 
5.1%
r9038
 
4.5%
T8471
 
4.2%
a8117
 
4.0%
i7441
 
3.7%
Other values (25)102402
50.8%
Common
ValueCountFrequency (%)
333917
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII235454
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
333917
 
14.4%
P12196
 
5.2%
A11637
 
4.9%
u11010
 
4.7%
C10519
 
4.5%
n10511
 
4.5%
R10195
 
4.3%
r9038
 
3.8%
T8471
 
3.6%
a8117
 
3.4%
Other values (26)109843
46.7%

coverages
Categorical

HIGH CORRELATION

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
{'H': 0.25}
5274 
{'C': 0.25, 'H': 0.25, 'O': 0.25}
4485 
{'N': 0.25}
4306 
{'O': 0.25}
4270 
{'C': 0.25, 'H': 0.25}
3760 
Other values (26)
30697 

Length

Max length37
Median length36
Mean length18.64303304
Min length11

Characters and Unicode

Total characters984203
Distinct characters16
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row{'CH2': 0.25}
2nd row{'CH2': 0.25}
3rd row{'CH2': 0.25}
4th row{'CH3': 0.25}
5th row{'CH3': 0.25}

Common Values

ValueCountFrequency (%)
{'H': 0.25}5274
 
10.0%
{'C': 0.25, 'H': 0.25, 'O': 0.25}4485
 
8.5%
{'N': 0.25}4306
 
8.2%
{'O': 0.25}4270
 
8.1%
{'C': 0.25, 'H': 0.25}3760
 
7.1%
{'S': 0.25}3465
 
6.6%
{'C': 0.25}3098
 
5.9%
{'N': 0.25, 'O': 0.25}2829
 
5.4%
{'H': 0.25, 'N': 0.25}1994
 
3.8%
{'H': 0.25, 'O': 0.25}1930
 
3.7%
Other values (21)17381
32.9%

Length

2022-10-11T12:49:22.835447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0.2588389
50.0%
h24159
 
13.7%
o18190
 
10.3%
c17034
 
9.6%
s10297
 
5.8%
n10265
 
5.8%
ch1442
 
0.8%
sh1437
 
0.8%
ch21390
 
0.8%
nh1284
 
0.7%
Other values (3)2891
 
1.6%

Most occurring characters

ValueCountFrequency (%)
'176778
18.0%
123986
12.6%
290822
9.2%
:88389
9.0%
088389
9.0%
.88389
9.0%
588389
9.0%
{52792
 
5.4%
}52792
 
5.4%
,35597
 
3.6%
Other values (6)97880
9.9%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation389153
39.5%
Decimal Number268647
27.3%
Space Separator123986
 
12.6%
Uppercase Letter96833
 
9.8%
Open Punctuation52792
 
5.4%
Close Punctuation52792
 
5.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
H32603
33.7%
C20913
21.6%
O20034
20.7%
S11734
 
12.1%
N11549
 
11.9%
Other Punctuation
ValueCountFrequency (%)
'176778
45.4%
:88389
22.7%
.88389
22.7%
,35597
 
9.1%
Decimal Number
ValueCountFrequency (%)
290822
33.8%
088389
32.9%
588389
32.9%
31047
 
0.4%
Space Separator
ValueCountFrequency (%)
123986
100.0%
Open Punctuation
ValueCountFrequency (%)
{52792
100.0%
Close Punctuation
ValueCountFrequency (%)
}52792
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common887370
90.2%
Latin96833
 
9.8%

Most frequent character per script

Common
ValueCountFrequency (%)
'176778
19.9%
123986
14.0%
290822
10.2%
:88389
10.0%
088389
10.0%
.88389
10.0%
588389
10.0%
{52792
 
5.9%
}52792
 
5.9%
,35597
 
4.0%
Latin
ValueCountFrequency (%)
H32603
33.7%
C20913
21.6%
O20034
20.7%
S11734
 
12.1%
N11549
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII984203
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
'176778
18.0%
123986
12.6%
290822
9.2%
:88389
9.0%
088389
9.0%
.88389
9.0%
588389
9.0%
{52792
 
5.4%
}52792
 
5.4%
,35597
 
3.6%
Other values (6)97880
9.9%

equation
Categorical

HIGH CORRELATION

Distinct48
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
0.5H2(g) + * -> H*
4243 
H2S(g) -H2(g) + * -> S*
 
3465
0.5N2(g) + * -> N*
 
3298
H2O(g) -H2(g) + * -> O*
 
3297
CH4(g)-2.0H2(g) + * -> C*
 
3098
Other values (43)
35391 

Length

Max length34
Median length31
Mean length23.89490832
Min length17

Characters and Unicode

Total characters1261460
Distinct characters24
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCH4(g) -H2(g) + * -> CH2*
2nd rowCH4(g) -H2(g) + * -> CH2*
3rd rowCH4(g) -H2(g) + * -> CH2*
4th rowCH4(g)-0.5H2(g) + * -> CH3*
5th rowCH4(g)-0.5H2(g) + * -> CH3*

Common Values

ValueCountFrequency (%)
0.5H2(g) + * -> H*4243
 
8.0%
H2S(g) -H2(g) + * -> S*3465
 
6.6%
0.5N2(g) + * -> N*3298
 
6.2%
H2O(g) -H2(g) + * -> O*3297
 
6.2%
CH4(g)-2.0H2(g) + * -> C*3098
 
5.9%
H2S(g)-0.5H2(g) + * -> SH*1223
 
2.3%
0.5H2(g) + 0.5N2(g) + * -> NH*1066
 
2.0%
CH4(g)-1.5H2(g) + * -> CH*1035
 
2.0%
H2(g) + 2* -> 2H*1031
 
2.0%
H2O(g) + * -> H2O*1025
 
1.9%
Other values (38)30011
56.8%

Length

2022-10-11T12:49:22.975123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
166629
48.6%
c10604
 
3.1%
o9762
 
2.8%
s9371
 
2.7%
h2(g8800
 
2.6%
2h8693
 
2.5%
38551
 
2.5%
n8314
 
2.4%
2o6532
 
1.9%
2c6430
 
1.9%
Other values (57)99245
28.9%

Most occurring characters

ValueCountFrequency (%)
290139
23.0%
*141181
11.2%
H97310
 
7.7%
289160
 
7.1%
+88393
 
7.0%
(67546
 
5.4%
g67546
 
5.4%
)67546
 
5.4%
-67542
 
5.4%
>52792
 
4.2%
Other values (14)232305
18.4%

Most occurring categories

ValueCountFrequency (%)
Space Separator290139
23.0%
Uppercase Letter230930
18.3%
Decimal Number171191
13.6%
Other Punctuation157835
12.5%
Math Symbol141185
11.2%
Open Punctuation67546
 
5.4%
Lowercase Letter67546
 
5.4%
Close Punctuation67546
 
5.4%
Dash Punctuation67542
 
5.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
289160
52.1%
320794
 
12.1%
417232
 
10.1%
015619
 
9.1%
515393
 
9.0%
66460
 
3.8%
71845
 
1.1%
81837
 
1.1%
91816
 
1.1%
11035
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
H97310
42.1%
C46136
20.0%
O41782
18.1%
S23040
 
10.0%
N22662
 
9.8%
Other Punctuation
ValueCountFrequency (%)
*141181
89.4%
.16654
 
10.6%
Math Symbol
ValueCountFrequency (%)
+88393
62.6%
>52792
37.4%
Space Separator
ValueCountFrequency (%)
290139
100.0%
Open Punctuation
ValueCountFrequency (%)
(67546
100.0%
Lowercase Letter
ValueCountFrequency (%)
g67546
100.0%
Close Punctuation
ValueCountFrequency (%)
)67546
100.0%
Dash Punctuation
ValueCountFrequency (%)
-67542
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common962984
76.3%
Latin298476
 
23.7%

Most frequent character per script

Common
ValueCountFrequency (%)
290139
30.1%
*141181
14.7%
289160
 
9.3%
+88393
 
9.2%
(67546
 
7.0%
)67546
 
7.0%
-67542
 
7.0%
>52792
 
5.5%
320794
 
2.2%
417232
 
1.8%
Other values (8)60659
 
6.3%
Latin
ValueCountFrequency (%)
H97310
32.6%
g67546
22.6%
C46136
15.5%
O41782
14.0%
S23040
 
7.7%
N22662
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1261460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
290139
23.0%
*141181
11.2%
H97310
 
7.7%
289160
 
7.1%
+88393
 
7.0%
(67546
 
5.4%
g67546
 
5.4%
)67546
 
5.4%
-67542
 
5.4%
>52792
 
4.2%
Other values (14)232305
18.4%

ads_1
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
C
17034 
H
12951 
N
7135 
O
5226 
S
3465 
Other values (7)
6981 

Length

Max length3
Median length1
Mean length1.187092741
Min length1

Characters and Unicode

Total characters62669
Distinct characters7
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCH2
2nd rowCH2
3rd rowCH2
4th rowCH3
5th rowCH3

Common Values

ValueCountFrequency (%)
C17034
32.3%
H12951
24.5%
N7135
13.5%
O5226
 
9.9%
S3465
 
6.6%
SH1223
 
2.3%
NH1066
 
2.0%
CH1035
 
2.0%
H2O1025
 
1.9%
CH21007
 
1.9%
Other values (2)1625
 
3.1%

Length

2022-10-11T12:49:23.107316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c17034
32.3%
h12951
24.5%
n7135
13.5%
o5226
 
9.9%
s3465
 
6.6%
sh1223
 
2.3%
nh1066
 
2.0%
ch1035
 
2.0%
h2o1025
 
1.9%
ch21007
 
1.9%
Other values (2)1625
 
3.1%

Most occurring characters

ValueCountFrequency (%)
C19940
31.8%
H19932
31.8%
N8201
13.1%
O7012
 
11.2%
S4688
 
7.5%
22032
 
3.2%
3864
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter59773
95.4%
Decimal Number2896
 
4.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C19940
33.4%
H19932
33.3%
N8201
13.7%
O7012
 
11.7%
S4688
 
7.8%
Decimal Number
ValueCountFrequency (%)
22032
70.2%
3864
29.8%

Most occurring scripts

ValueCountFrequency (%)
Latin59773
95.4%
Common2896
 
4.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
C19940
33.4%
H19932
33.3%
N8201
13.7%
O7012
 
11.7%
S4688
 
7.8%
Common
ValueCountFrequency (%)
22032
70.2%
3864
29.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII62669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C19940
31.8%
H19932
31.8%
N8201
13.1%
O7012
 
11.2%
S4688
 
7.5%
22032
 
3.2%
3864
 
1.4%

site_1
Categorical

HIGH CORRELATION

Distinct46
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
hollow,
19544 
hollow|A_A_A|HCP
3415 
hollow|A_A_A|FCC
3267 
bridge,
3138 
hollow|A_A_B|HCP
3015 
Other values (41)
20413 

Length

Max length21
Median length18
Mean length9.882917866
Min length3

Characters and Unicode

Total characters521739
Distinct characters24
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowbridge|A_A|A
2nd rowhollow|A_A_A|FCC
3rd rowtop|A
4th rowbridge|A_A|A
5th rowhollow|A_A_A|FCC

Common Values

ValueCountFrequency (%)
hollow,19544
37.0%
hollow|A_A_A|HCP3415
 
6.5%
hollow|A_A_A|FCC3267
 
6.2%
bridge,3138
 
5.9%
hollow|A_A_B|HCP3015
 
5.7%
hollow|A_A_B|FCC3009
 
5.7%
top|B2812
 
5.3%
hollow2298
 
4.4%
top|A1578
 
3.0%
top,1345
 
2.5%
Other values (36)9371
17.8%

Length

2022-10-11T12:49:23.239100image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hollow21842
41.4%
bridge3489
 
6.6%
hollow|a_a_a|hcp3415
 
6.5%
hollow|a_a_a|fcc3267
 
6.2%
hollow|a_a_b|hcp3015
 
5.7%
hollow|a_a_b|fcc3009
 
5.7%
top|b2812
 
5.3%
top|a1578
 
3.0%
top1578
 
3.0%
bridge|a_a|b1274
 
2.4%
Other values (29)7513
 
14.2%

Most occurring characters

ValueCountFrequency (%)
o83547
16.0%
l78959
15.1%
A44778
8.6%
|43770
8.4%
h38059
7.3%
w38059
7.3%
_35821
 
6.9%
,25398
 
4.9%
C23524
 
4.5%
B19166
 
3.7%
Other values (14)90658
17.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter303024
58.1%
Uppercase Letter110885
 
21.3%
Math Symbol43770
 
8.4%
Connector Punctuation35821
 
6.9%
Other Punctuation25398
 
4.9%
Dash Punctuation1906
 
0.4%
Decimal Number935
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o83547
27.6%
l78959
26.1%
h38059
12.6%
w38059
12.6%
t10306
 
3.4%
i9210
 
3.0%
d8239
 
2.7%
e7304
 
2.4%
b7304
 
2.4%
g7304
 
2.4%
Other values (3)14733
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
A44778
40.4%
C23524
21.2%
B19166
17.3%
F7877
 
7.1%
P7770
 
7.0%
H7770
 
7.0%
Math Symbol
ValueCountFrequency (%)
|43770
100.0%
Connector Punctuation
ValueCountFrequency (%)
_35821
100.0%
Other Punctuation
ValueCountFrequency (%)
,25398
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1906
100.0%
Decimal Number
ValueCountFrequency (%)
4935
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin413909
79.3%
Common107830
 
20.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o83547
20.2%
l78959
19.1%
A44778
10.8%
h38059
9.2%
w38059
9.2%
C23524
 
5.7%
B19166
 
4.6%
t10306
 
2.5%
i9210
 
2.2%
d8239
 
2.0%
Other values (9)60062
14.5%
Common
ValueCountFrequency (%)
|43770
40.6%
_35821
33.2%
,25398
23.6%
-1906
 
1.8%
4935
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII521739
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o83547
16.0%
l78959
15.1%
A44778
8.6%
|43770
8.4%
h38059
7.3%
w38059
7.3%
_35821
 
6.9%
,25398
 
4.9%
C23524
 
4.5%
B19166
 
3.7%
Other values (14)90658
17.4%

site_2
Categorical

HIGH CORRELATION

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
0.0
27394 
hollow
11899 
hollow,
6984 
bridge,
 
1717
bridge
 
1639
Other values (10)
3159 

Length

Max length12
Median length3
Mean length4.585713745
Min length3

Characters and Unicode

Total characters242089
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.027394
51.9%
hollow11899
22.5%
hollow,6984
 
13.2%
bridge,1717
 
3.3%
bridge1639
 
3.1%
top,1098
 
2.1%
top1061
 
2.0%
hollow-tilt355
 
0.7%
hollow-tilt,210
 
0.4%
top-tilt,127
 
0.2%
Other values (5)308
 
0.6%

Length

2022-10-11T12:49:23.369147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0.027394
51.9%
hollow18883
35.8%
bridge3356
 
6.4%
top2159
 
4.1%
hollow-tilt565
 
1.1%
top-tilt231
 
0.4%
bridge-tilt119
 
0.2%
4fold85
 
0.2%

Most occurring characters

ValueCountFrequency (%)
054788
22.6%
o41371
17.1%
l39896
16.5%
.27394
11.3%
h19448
 
8.0%
w19448
 
8.0%
,10199
 
4.2%
i4390
 
1.8%
t4220
 
1.7%
d3560
 
1.5%
Other values (8)17375
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter148708
61.4%
Decimal Number54873
 
22.7%
Other Punctuation37593
 
15.5%
Dash Punctuation915
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o41371
27.8%
l39896
26.8%
h19448
13.1%
w19448
13.1%
i4390
 
3.0%
t4220
 
2.8%
d3560
 
2.4%
r3475
 
2.3%
b3475
 
2.3%
g3475
 
2.3%
Other values (3)5950
 
4.0%
Decimal Number
ValueCountFrequency (%)
054788
99.8%
485
 
0.2%
Other Punctuation
ValueCountFrequency (%)
.27394
72.9%
,10199
 
27.1%
Dash Punctuation
ValueCountFrequency (%)
-915
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin148708
61.4%
Common93381
38.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
o41371
27.8%
l39896
26.8%
h19448
13.1%
w19448
13.1%
i4390
 
3.0%
t4220
 
2.8%
d3560
 
2.4%
r3475
 
2.3%
b3475
 
2.3%
g3475
 
2.3%
Other values (3)5950
 
4.0%
Common
ValueCountFrequency (%)
054788
58.7%
.27394
29.3%
,10199
 
10.9%
-915
 
1.0%
485
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII242089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
054788
22.6%
o41371
17.1%
l39896
16.5%
.27394
11.3%
h19448
 
8.0%
w19448
 
8.0%
,10199
 
4.2%
i4390
 
1.8%
t4220
 
1.7%
d3560
 
1.5%
Other values (8)17375
 
7.2%

ads_2
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
0.0
27394 
H
11208 
O
8479 
S
3098 
N
 
2212
Other values (3)
 
401

Length

Max length3
Median length3
Mean length2.048871041
Min length1

Characters and Unicode

Total characters108164
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.027394
51.9%
H11208
21.2%
O8479
 
16.1%
S3098
 
5.9%
N2212
 
4.2%
CH200
 
0.4%
CH2183
 
0.3%
OH18
 
< 0.1%

Length

2022-10-11T12:49:23.497489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-11T12:49:23.662354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.027394
51.9%
h11208
21.2%
o8479
 
16.1%
s3098
 
5.9%
n2212
 
4.2%
ch200
 
0.4%
ch2183
 
0.3%
oh18
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
054788
50.7%
.27394
25.3%
H11609
 
10.7%
O8497
 
7.9%
S3098
 
2.9%
N2212
 
2.0%
C383
 
0.4%
2183
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number54971
50.8%
Other Punctuation27394
25.3%
Uppercase Letter25799
23.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
H11609
45.0%
O8497
32.9%
S3098
 
12.0%
N2212
 
8.6%
C383
 
1.5%
Decimal Number
ValueCountFrequency (%)
054788
99.7%
2183
 
0.3%
Other Punctuation
ValueCountFrequency (%)
.27394
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common82365
76.1%
Latin25799
 
23.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
H11609
45.0%
O8497
32.9%
S3098
 
12.0%
N2212
 
8.6%
C383
 
1.5%
Common
ValueCountFrequency (%)
054788
66.5%
.27394
33.3%
2183
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII108164
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
054788
50.7%
.27394
25.3%
H11609
 
10.7%
O8497
 
7.9%
S3098
 
2.9%
N2212
 
2.0%
C383
 
0.4%
2183
 
0.2%

site_3
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
0.0
42593 
hollow
8286 
bridge
 
955
top
 
587
hollow-tilt
 
239
Other values (3)
 
132

Length

Max length11
Median length3
Mean length3.574708289
Min length3

Characters and Unicode

Total characters188716
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.042593
80.7%
hollow8286
 
15.7%
bridge955
 
1.8%
top587
 
1.1%
hollow-tilt239
 
0.5%
top-tilt53
 
0.1%
bridge-tilt47
 
0.1%
4fold32
 
0.1%

Length

2022-10-11T12:49:23.790079image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-11T12:49:23.929113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.042593
80.7%
hollow8286
 
15.7%
bridge955
 
1.8%
top587
 
1.1%
hollow-tilt239
 
0.5%
top-tilt53
 
0.1%
bridge-tilt47
 
0.1%
4fold32
 
0.1%

Most occurring characters

ValueCountFrequency (%)
085186
45.1%
.42593
22.6%
o17722
 
9.4%
l17421
 
9.2%
h8525
 
4.5%
w8525
 
4.5%
i1341
 
0.7%
t1318
 
0.7%
d1034
 
0.5%
r1002
 
0.5%
Other values (7)4049
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number85218
45.2%
Lowercase Letter60566
32.1%
Other Punctuation42593
22.6%
Dash Punctuation339
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o17722
29.3%
l17421
28.8%
h8525
14.1%
w8525
14.1%
i1341
 
2.2%
t1318
 
2.2%
d1034
 
1.7%
r1002
 
1.7%
b1002
 
1.7%
g1002
 
1.7%
Other values (3)1674
 
2.8%
Decimal Number
ValueCountFrequency (%)
085186
> 99.9%
432
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.42593
100.0%
Dash Punctuation
ValueCountFrequency (%)
-339
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common128150
67.9%
Latin60566
32.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o17722
29.3%
l17421
28.8%
h8525
14.1%
w8525
14.1%
i1341
 
2.2%
t1318
 
2.2%
d1034
 
1.7%
r1002
 
1.7%
b1002
 
1.7%
g1002
 
1.7%
Other values (3)1674
 
2.8%
Common
ValueCountFrequency (%)
085186
66.5%
.42593
33.2%
-339
 
0.3%
432
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII188716
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
085186
45.1%
.42593
22.6%
o17722
 
9.4%
l17421
 
9.2%
h8525
 
4.5%
w8525
 
4.5%
i1341
 
0.7%
t1318
 
0.7%
d1034
 
0.5%
r1002
 
0.5%
Other values (7)4049
 
2.1%

ads_3
Categorical

HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
0.0
42593 
O
4485 
S
 
3734
N
 
918
NH
 
218
Other values (6)
 
844

Length

Max length3
Median length3
Mean length2.641328232
Min length1

Characters and Unicode

Total characters139441
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.042593
80.7%
O4485
 
8.5%
S3734
 
7.1%
N918
 
1.7%
NH218
 
0.4%
SH214
 
0.4%
CH207
 
0.4%
CH2200
 
0.4%
CH3183
 
0.3%
OH22
 
< 0.1%

Length

2022-10-11T12:49:24.053653image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0.042593
80.7%
o4485
 
8.5%
s3734
 
7.1%
n918
 
1.7%
nh218
 
0.4%
sh214
 
0.4%
ch207
 
0.4%
ch2200
 
0.4%
ch3183
 
0.3%
oh22
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
085186
61.1%
.42593
30.5%
O4525
 
3.2%
S3948
 
2.8%
N1136
 
0.8%
H1062
 
0.8%
C590
 
0.4%
2218
 
0.2%
3183
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number85587
61.4%
Other Punctuation42593
30.5%
Uppercase Letter11261
 
8.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O4525
40.2%
S3948
35.1%
N1136
 
10.1%
H1062
 
9.4%
C590
 
5.2%
Decimal Number
ValueCountFrequency (%)
085186
99.5%
2218
 
0.3%
3183
 
0.2%
Other Punctuation
ValueCountFrequency (%)
.42593
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common128180
91.9%
Latin11261
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
O4525
40.2%
S3948
35.1%
N1136
 
10.1%
H1062
 
9.4%
C590
 
5.2%
Common
ValueCountFrequency (%)
085186
66.5%
.42593
33.2%
2218
 
0.2%
3183
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII139441
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
085186
61.1%
.42593
30.5%
O4525
 
3.2%
S3948
 
2.8%
N1136
 
0.8%
H1062
 
0.8%
C590
 
0.4%
2218
 
0.2%
3183
 
0.1%

band_gap
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00299034134
Minimum0
Maximum0.9674
Zeros52455
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size412.6 KiB
2022-10-11T12:49:24.167750image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.9674
Range0.9674
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0458905319
Coefficient of variation (CV)15.34625205
Kurtosis318.7609768
Mean0.00299034134
Median Absolute Deviation (MAD)0
Skewness17.37941656
Sum157.8661
Variance0.002105940918
MonotonicityNot monotonic
2022-10-11T12:49:24.279610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
052455
99.4%
0.722766
 
0.1%
0.967461
 
0.1%
0.109447
 
0.1%
0.023144
 
0.1%
0.57541
 
0.1%
0.246539
 
0.1%
0.301932
 
0.1%
0.3077
 
< 0.1%
ValueCountFrequency (%)
052455
99.4%
0.023144
 
0.1%
0.109447
 
0.1%
0.246539
 
0.1%
0.301932
 
0.1%
0.3077
 
< 0.1%
0.57541
 
0.1%
0.722766
 
0.1%
0.967461
 
0.1%
ValueCountFrequency (%)
0.967461
 
0.1%
0.722766
 
0.1%
0.57541
 
0.1%
0.3077
 
< 0.1%
0.301932
 
0.1%
0.246539
 
0.1%
0.109447
 
0.1%
0.023144
 
0.1%
052455
99.4%

efermi
Real number (ℝ)

HIGH CORRELATION

Distinct1088
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.237244742
Minimum-9.80862143
Maximum10.58671474
Zeros0
Zeros (%)0.0%
Negative99
Negative (%)0.2%
Memory size412.6 KiB
2022-10-11T12:49:24.424276image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-9.80862143
5-th percentile3.48601531
Q15.04334495
median6.36227169
Q37.53498815
95-th percentile8.78966032
Maximum10.58671474
Range20.39533617
Interquartile range (IQR)2.4916432

Descriptive statistics

Standard deviation1.783123461
Coefficient of variation (CV)0.285883196
Kurtosis10.97321376
Mean6.237244742
Median Absolute Deviation (MAD)1.24975897
Skewness-1.436873164
Sum329276.6244
Variance3.179529277
MonotonicityNot monotonic
2022-10-11T12:49:24.583348image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.96891416112
 
0.2%
5.44965698111
 
0.2%
4.55391003108
 
0.2%
3.13875955108
 
0.2%
9.12996087107
 
0.2%
3.73492099107
 
0.2%
7.46216787106
 
0.2%
4.22413457105
 
0.2%
7.19441606104
 
0.2%
8.47822675104
 
0.2%
Other values (1078)51720
98.0%
ValueCountFrequency (%)
-9.8086214399
0.2%
0.081742037
 
< 0.1%
1.6987508842
0.1%
1.9018313743
0.1%
2.3668138514
 
< 0.1%
2.4642719238
 
0.1%
2.6550806342
0.1%
2.7405887228
 
0.1%
2.749432587
 
< 0.1%
2.8378670846
0.1%
ValueCountFrequency (%)
10.5867147478
0.1%
10.152277177
0.1%
9.8956408430
 
0.1%
9.8366444458
0.1%
9.784768157
0.1%
9.7171021562
0.1%
9.66588868102
0.2%
9.6152194266
0.1%
9.606634445
0.1%
9.5692896941
0.1%

formation_energy_per_atom
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct1053
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.1120311436
Minimum-1.256620705
Maximum2.412463175
Zeros1824
Zeros (%)3.5%
Negative31872
Negative (%)60.4%
Memory size412.6 KiB
2022-10-11T12:49:24.741892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1.256620705
5-th percentile-0.683686925
Q1-0.2761814775
median-0.05732447
Q30.05043035
95-th percentile0.299289685
Maximum2.412463175
Range3.66908388
Interquartile range (IQR)0.3266118275

Descriptive statistics

Standard deviation0.321352609
Coefficient of variation (CV)-2.868422106
Kurtosis8.036684599
Mean-0.1120311436
Median Absolute Deviation (MAD)0.144063895
Skewness0.5481367581
Sum-5914.348135
Variance0.1032674993
MonotonicityNot monotonic
2022-10-11T12:49:24.910622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01824
 
3.5%
0.084207955112
 
0.2%
-0.0174476111
 
0.2%
-0.079670265108
 
0.2%
-0.0526618075108
 
0.2%
-0.02748068107
 
0.2%
0.0856875975107
 
0.2%
0.06940541125106
 
0.2%
-0.0686117975105
 
0.2%
-0.0562460525104
 
0.2%
Other values (1043)50000
94.7%
ValueCountFrequency (%)
-1.25662070543
0.1%
-1.22749574247
0.1%
-1.18255308239
0.1%
-1.14682320844
0.1%
-1.1016516349
0.1%
-1.08962948840
0.1%
-1.0830554842
0.1%
-1.05846782339
0.1%
-1.04734848336
0.1%
-1.04340177746
0.1%
ValueCountFrequency (%)
2.41246317599
0.2%
1.44807692546
0.1%
1.02860560441
0.1%
0.98816568876
 
< 0.1%
0.97321065758
 
< 0.1%
0.97229661127
 
< 0.1%
0.915298337540
0.1%
0.894677221353
0.1%
0.867780449
0.1%
0.861058942511
 
< 0.1%

total_magnetization
Real number (ℝ≥0)

Distinct1068
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3508462025
Minimum0
Maximum7.9663089
Zeros39
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size412.6 KiB
2022-10-11T12:49:25.085473image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.325 × 10-6
Q10.00012855
median0.0014032
Q30.0208722
95-th percentile2.5444076
Maximum7.9663089
Range7.9663089
Interquartile range (IQR)0.02074365

Descriptive statistics

Standard deviation1.045757477
Coefficient of variation (CV)2.980672069
Kurtosis17.92307511
Mean0.3508462025
Median Absolute Deviation (MAD)0.0013896
Skewness4.00794108
Sum18521.87273
Variance1.0936087
MonotonicityNot monotonic
2022-10-11T12:49:25.245959image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.69 × 10-5178
 
0.3%
9.9 × 10-6170
 
0.3%
6 × 10-7149
 
0.3%
1 × 10-6140
 
0.3%
0.0001352137
 
0.3%
3 × 10-7136
 
0.3%
2.88 × 10-5131
 
0.2%
5.4 × 10-6129
 
0.2%
0.7433976112
 
0.2%
3.35 × 10-5111
 
0.2%
Other values (1058)51399
97.4%
ValueCountFrequency (%)
039
 
0.1%
1 × 10-761
0.1%
3 × 10-7136
0.3%
3.75 × 10-740
 
0.1%
4 × 10-783
0.2%
5 × 10-749
 
0.1%
6 × 10-7149
0.3%
1 × 10-6140
0.3%
1.1 × 10-646
 
0.1%
1.15 × 10-645
 
0.1%
ValueCountFrequency (%)
7.966308918
 
< 0.1%
7.768258918
 
< 0.1%
7.7424222
 
< 0.1%
7.405834442
0.1%
7.23930981
0.2%
6.976307174
0.1%
6.892532685
0.2%
6.7325673541
0.1%
6.724463569
0.1%
5.602107980
0.2%

volume
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1088
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.80752253
Minimum11.45377624
Maximum689.1733355
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size412.6 KiB
2022-10-11T12:49:25.402091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum11.45377624
5-th percentile25.91939377
Q146.09223221
median69.12311898
Q3116.7809607
95-th percentile249.7204977
Maximum689.1733355
Range677.7195593
Interquartile range (IQR)70.68872845

Descriptive statistics

Standard deviation82.5260848
Coefficient of variation (CV)0.8613737483
Kurtosis10.08973321
Mean95.80752253
Median Absolute Deviation (MAD)36.14041477
Skewness2.73395935
Sum5057870.729
Variance6810.554672
MonotonicityNot monotonic
2022-10-11T12:49:25.564224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61.6885434112
 
0.2%
66.59195207111
 
0.2%
69.85166327108
 
0.2%
69.22262914108
 
0.2%
57.88226099107
 
0.2%
132.6679323107
 
0.2%
114.5216777106
 
0.2%
58.3182262105
 
0.2%
61.51741323104
 
0.2%
28.29488068104
 
0.2%
Other values (1078)51720
98.0%
ValueCountFrequency (%)
11.4537762439
0.1%
11.8718896475
0.1%
13.3995939660
0.1%
14.1989285429
 
0.1%
14.5545867930
 
0.1%
15.490302276
0.1%
15.7228557629
 
0.1%
15.8916287528
 
0.1%
16.191438465
0.1%
16.4717213866
0.1%
ValueCountFrequency (%)
689.173335540
0.1%
676.88344835
 
< 0.1%
587.894708799
0.2%
539.657445165
0.1%
524.706646351
0.1%
514.303528524
 
< 0.1%
508.201305545
0.1%
500.284597543
0.1%
481.382625526
 
< 0.1%
453.968402442
0.1%

energy_per_atom
Real number (ℝ)

Distinct1088
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.371581269
Minimum-12.95812647
Maximum-0.30362902
Zeros0
Zeros (%)0.0%
Negative52792
Negative (%)100.0%
Memory size412.6 KiB
2022-10-11T12:49:25.726734image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-12.95812647
5-th percentile-10.7503485
Q1-8.391521567
median-6.11278359
Q3-4.408225837
95-th percentile-2.510389725
Maximum-0.30362902
Range12.65449745
Interquartile range (IQR)3.98329573

Descriptive statistics

Standard deviation2.594581331
Coefficient of variation (CV)-0.4072115259
Kurtosis-0.7372924102
Mean-6.371581269
Median Absolute Deviation (MAD)2.02842972
Skewness-0.1936374477
Sum-336368.5184
Variance6.731852282
MonotonicityNot monotonic
2022-10-11T12:49:26.337379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-6.678580433112
 
0.2%
-3.497660423111
 
0.2%
-3.829203155108
 
0.2%
-3.471180335108
 
0.2%
-8.490930885107
 
0.2%
-3.063510862107
 
0.2%
-8.241310371106
 
0.2%
-4.97577867105
 
0.2%
-6.397875965104
 
0.2%
-10.26032559104
 
0.2%
Other values (1078)51720
98.0%
ValueCountFrequency (%)
-12.9581264765
0.1%
-12.8105765359
0.1%
-12.7732758636
0.1%
-12.4754886536
0.1%
-12.4454257259
0.1%
-12.4445271927
0.1%
-12.349010245
0.1%
-12.2225911935
0.1%
-12.1993137450
0.1%
-12.1756695855
0.1%
ValueCountFrequency (%)
-0.303629027
 
< 0.1%
-0.42273548542
0.1%
-0.427271042543
0.1%
-0.822937787528
0.1%
-0.855104507548
0.1%
-0.9062027838
0.1%
-0.911093751345
0.1%
-0.918544508835
0.1%
-0.919700036214
 
< 0.1%
-0.92953308877
 
< 0.1%

ads_IE_1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.46275486
Minimum9.84
Maximum14.53414
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size412.6 KiB
2022-10-11T12:49:26.462366image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum9.84
5-th percentile10.36001
Q111.2603
median12.8
Q313.59844
95-th percentile14.53414
Maximum14.53414
Range4.69414
Interquartile range (IQR)2.33814

Descriptive statistics

Standard deviation1.465710255
Coefficient of variation (CV)0.1176072443
Kurtosis-1.532507804
Mean12.46275486
Median Absolute Deviation (MAD)1.5397
Skewness-0.07432118475
Sum657933.7545
Variance2.148306552
MonotonicityNot monotonic
2022-10-11T12:49:26.577297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11.260317034
32.3%
13.5984412951
24.5%
14.534147135
13.5%
13.618065226
 
9.9%
10.360013465
 
6.6%
10.42191223
 
2.3%
12.81066
 
2.0%
10.641035
 
2.0%
12.651025
 
1.9%
10.3961007
 
1.9%
Other values (2)1625
 
3.1%
ValueCountFrequency (%)
9.84864
 
1.6%
10.360013465
 
6.6%
10.3961007
 
1.9%
10.42191223
 
2.3%
10.641035
 
2.0%
11.260317034
32.3%
12.651025
 
1.9%
12.81066
 
2.0%
13.017761
 
1.4%
13.5984412951
24.5%
ValueCountFrequency (%)
14.534147135
13.5%
13.618065226
 
9.9%
13.5984412951
24.5%
13.017761
 
1.4%
12.81066
 
2.0%
12.651025
 
1.9%
11.260317034
32.3%
10.641035
 
2.0%
10.42191223
 
2.3%
10.3961007
 
1.9%

ads_H_1
Real number (ℝ)

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean419.5694135
Minimum-241.826
Maximum716.68
Zeros0
Zeros (%)0.0%
Negative1025
Negative (%)1.9%
Memory size412.6 KiB
2022-10-11T12:49:26.683728image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-241.826
5-th percentile139.33
Q1217.998
median376.56
Q3716.68
95-th percentile716.68
Maximum716.68
Range958.506
Interquartile range (IQR)498.682

Descriptive statistics

Standard deviation239.419656
Coefficient of variation (CV)0.5706318152
Kurtosis-0.7776218387
Mean419.5694135
Median Absolute Deviation (MAD)158.562
Skewness-0.09922912723
Sum22149908.48
Variance57321.7717
MonotonicityNot monotonic
2022-10-11T12:49:26.796183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
716.6817034
32.3%
217.99812951
24.5%
472.687135
13.5%
249.185226
 
9.9%
277.173465
 
6.6%
139.331223
 
2.3%
376.561066
 
2.0%
594.131035
 
2.0%
-241.8261025
 
1.9%
386.391007
 
1.9%
Other values (2)1625
 
3.1%
ValueCountFrequency (%)
-241.8261025
 
1.9%
38.99761
 
1.4%
139.331223
 
2.3%
145.69864
 
1.6%
217.99812951
24.5%
249.185226
9.9%
277.173465
 
6.6%
376.561066
 
2.0%
386.391007
 
1.9%
472.687135
13.5%
ValueCountFrequency (%)
716.6817034
32.3%
594.131035
 
2.0%
472.687135
13.5%
386.391007
 
1.9%
376.561066
 
2.0%
277.173465
 
6.6%
249.185226
 
9.9%
217.99812951
24.5%
145.69864
 
1.6%
139.331223
 
2.3%

ads_S_1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151.8056464
Minimum114.717
Maximum195.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size412.6 KiB
2022-10-11T12:49:26.907185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum114.717
5-th percentile114.717
Q1153.301
median158.1
Q3161.059
95-th percentile193.93
Maximum195.63
Range80.913
Interquartile range (IQR)7.758

Descriptive statistics

Standard deviation23.69644773
Coefficient of variation (CV)0.1560972749
Kurtosis-0.6108800616
Mean151.8056464
Median Absolute Deviation (MAD)4.799
Skewness-0.3682416892
Sum8014123.686
Variance561.5216349
MonotonicityNot monotonic
2022-10-11T12:49:27.024069image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
158.117034
32.3%
114.71712951
24.5%
153.3017135
13.5%
161.0595226
 
9.9%
167.8293465
 
6.6%
195.631223
 
2.3%
181.251066
 
2.0%
183.041035
 
2.0%
188.8351025
 
1.9%
193.931007
 
1.9%
Other values (2)1625
 
3.1%
ValueCountFrequency (%)
114.71712951
24.5%
153.3017135
13.5%
158.117034
32.3%
161.0595226
 
9.9%
167.8293465
 
6.6%
181.251066
 
2.0%
183.041035
 
2.0%
183.71761
 
1.4%
188.8351025
 
1.9%
193.931007
 
1.9%
ValueCountFrequency (%)
195.631223
 
2.3%
194.17864
 
1.6%
193.931007
 
1.9%
188.8351025
 
1.9%
183.71761
 
1.4%
183.041035
 
2.0%
181.251066
 
2.0%
167.8293465
 
6.6%
161.0595226
 
9.9%
158.117034
32.3%

ads_IE_2
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.371958799
Minimum0
Maximum14.53414
Zeros27394
Zeros (%)51.9%
Negative0
Negative (%)0.0%
Memory size412.6 KiB
2022-10-11T12:49:27.133514image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q313.59844
95-th percentile13.61806
Maximum14.53414
Range14.53414
Interquartile range (IQR)13.59844

Descriptive statistics

Standard deviation6.667409876
Coefficient of variation (CV)1.046367387
Kurtosis-1.947077419
Mean6.371958799
Median Absolute Deviation (MAD)0
Skewness0.1150764544
Sum336388.4489
Variance44.45435446
MonotonicityNot monotonic
2022-10-11T12:49:27.239465image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
027394
51.9%
13.5984411208
21.2%
13.618068479
 
16.1%
10.360013098
 
5.9%
14.534142212
 
4.2%
10.64200
 
0.4%
10.396183
 
0.3%
13.01718
 
< 0.1%
ValueCountFrequency (%)
027394
51.9%
10.360013098
 
5.9%
10.396183
 
0.3%
10.64200
 
0.4%
13.01718
 
< 0.1%
13.5984411208
21.2%
13.618068479
 
16.1%
14.534142212
 
4.2%
ValueCountFrequency (%)
14.534142212
 
4.2%
13.618068479
 
16.1%
13.5984411208
21.2%
13.01718
 
< 0.1%
10.64200
 
0.4%
10.396183
 
0.3%
10.360013098
 
5.9%
027394
51.9%

ads_H_2
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.9773605
Minimum0
Maximum594.13
Zeros27394
Zeros (%)51.9%
Negative0
Negative (%)0.0%
Memory size412.6 KiB
2022-10-11T12:49:27.338138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3249.18
95-th percentile277.17
Maximum594.13
Range594.13
Interquartile range (IQR)249.18

Descriptive statistics

Standard deviation141.1280957
Coefficient of variation (CV)1.120265539
Kurtosis-0.4363149311
Mean125.9773605
Median Absolute Deviation (MAD)0
Skewness0.6500277594
Sum6650596.814
Variance19917.13939
MonotonicityNot monotonic
2022-10-11T12:49:27.442591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
027394
51.9%
217.99811208
21.2%
249.188479
 
16.1%
277.173098
 
5.9%
472.682212
 
4.2%
594.13200
 
0.4%
386.39183
 
0.3%
38.9918
 
< 0.1%
ValueCountFrequency (%)
027394
51.9%
38.9918
 
< 0.1%
217.99811208
21.2%
249.188479
 
16.1%
277.173098
 
5.9%
386.39183
 
0.3%
472.682212
 
4.2%
594.13200
 
0.4%
ValueCountFrequency (%)
594.13200
 
0.4%
472.682212
 
4.2%
386.39183
 
0.3%
277.173098
 
5.9%
249.188479
 
16.1%
217.99811208
21.2%
38.9918
 
< 0.1%
027394
51.9%

ads_S_2
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.92331075
Minimum0
Maximum193.93
Zeros27394
Zeros (%)51.9%
Negative0
Negative (%)0.0%
Memory size412.6 KiB
2022-10-11T12:49:27.547172image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3153.301
95-th percentile167.829
Maximum193.93
Range193.93
Interquartile range (IQR)153.301

Descriptive statistics

Standard deviation72.4784626
Coefficient of variation (CV)1.06706316
Kurtosis-1.774413381
Mean67.92331075
Median Absolute Deviation (MAD)0
Skewness0.2278262607
Sum3585807.421
Variance5253.127541
MonotonicityNot monotonic
2022-10-11T12:49:27.661258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
027394
51.9%
114.71711208
21.2%
161.0598479
 
16.1%
167.8293098
 
5.9%
153.3012212
 
4.2%
183.04200
 
0.4%
193.93183
 
0.3%
183.7118
 
< 0.1%
ValueCountFrequency (%)
027394
51.9%
114.71711208
21.2%
153.3012212
 
4.2%
161.0598479
 
16.1%
167.8293098
 
5.9%
183.04200
 
0.4%
183.7118
 
< 0.1%
193.93183
 
0.3%
ValueCountFrequency (%)
193.93183
 
0.3%
183.7118
 
< 0.1%
183.04200
 
0.4%
167.8293098
 
5.9%
161.0598479
 
16.1%
153.3012212
 
4.2%
114.71711208
21.2%
027394
51.9%

ads_IE_3
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.362493892
Minimum0
Maximum14.53414
Zeros42593
Zeros (%)80.7%
Negative0
Negative (%)0.0%
Memory size412.6 KiB
2022-10-11T12:49:27.774329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile13.61806
Maximum14.53414
Range14.53414
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.885700869
Coefficient of variation (CV)2.068026878
Kurtosis0.8263594977
Mean2.362493892
Median Absolute Deviation (MAD)0
Skewness1.641038067
Sum124720.7776
Variance23.87007298
MonotonicityNot monotonic
2022-10-11T12:49:27.892043image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
042593
80.7%
13.618064485
 
8.5%
10.360013734
 
7.1%
14.53414918
 
1.7%
12.8218
 
0.4%
10.4219214
 
0.4%
10.64207
 
0.4%
10.396200
 
0.4%
9.84183
 
0.3%
13.01722
 
< 0.1%
ValueCountFrequency (%)
042593
80.7%
9.84183
 
0.3%
10.360013734
 
7.1%
10.396200
 
0.4%
10.4219214
 
0.4%
10.64207
 
0.4%
12.6518
 
< 0.1%
12.8218
 
0.4%
13.01722
 
< 0.1%
13.618064485
 
8.5%
ValueCountFrequency (%)
14.53414918
 
1.7%
13.618064485
8.5%
13.01722
 
< 0.1%
12.8218
 
0.4%
12.6518
 
< 0.1%
10.64207
 
0.4%
10.4219214
 
0.4%
10.396200
 
0.4%
10.360013734
7.1%
9.84183
 
0.3%

ads_H_3
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.34514911
Minimum-241.826
Maximum594.13
Zeros42593
Zeros (%)80.7%
Negative18
Negative (%)< 0.1%
Memory size412.6 KiB
2022-10-11T12:49:28.003924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-241.826
5-th percentile0
Q10
median0
Q30
95-th percentile277.17
Maximum594.13
Range835.956
Interquartile range (IQR)0

Descriptive statistics

Standard deviation119.3278771
Coefficient of variation (CV)2.156067496
Kurtosis3.161902774
Mean55.34514911
Median Absolute Deviation (MAD)0
Skewness2.002021356
Sum2921781.112
Variance14239.14225
MonotonicityNot monotonic
2022-10-11T12:49:28.110013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
042593
80.7%
249.184485
 
8.5%
277.173734
 
7.1%
472.68918
 
1.7%
376.56218
 
0.4%
139.33214
 
0.4%
594.13207
 
0.4%
386.39200
 
0.4%
145.69183
 
0.3%
38.9922
 
< 0.1%
ValueCountFrequency (%)
-241.82618
 
< 0.1%
042593
80.7%
38.9922
 
< 0.1%
139.33214
 
0.4%
145.69183
 
0.3%
249.184485
 
8.5%
277.173734
 
7.1%
376.56218
 
0.4%
386.39200
 
0.4%
472.68918
 
1.7%
ValueCountFrequency (%)
594.13207
 
0.4%
472.68918
 
1.7%
386.39200
 
0.4%
376.56218
 
0.4%
277.173734
 
7.1%
249.184485
 
8.5%
145.69183
 
0.3%
139.33214
 
0.4%
38.9922
 
< 0.1%
042593
80.7%

ads_S_3
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.0271969
Minimum0
Maximum195.63
Zeros42593
Zeros (%)80.7%
Negative0
Negative (%)0.0%
Memory size412.6 KiB
2022-10-11T12:49:28.215784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile167.829
Maximum195.63
Range195.63
Interquartile range (IQR)0

Descriptive statistics

Standard deviation65.57860794
Coefficient of variation (CV)2.047591244
Kurtosis0.4949292328
Mean32.0271969
Median Absolute Deviation (MAD)0
Skewness1.570027577
Sum1690779.779
Variance4300.553819
MonotonicityNot monotonic
2022-10-11T12:49:28.339693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
042593
80.7%
161.0594485
 
8.5%
167.8293734
 
7.1%
153.301918
 
1.7%
181.25218
 
0.4%
195.63214
 
0.4%
183.04207
 
0.4%
193.93200
 
0.4%
194.17183
 
0.3%
183.7122
 
< 0.1%
ValueCountFrequency (%)
042593
80.7%
153.301918
 
1.7%
161.0594485
 
8.5%
167.8293734
 
7.1%
181.25218
 
0.4%
183.04207
 
0.4%
183.7122
 
< 0.1%
188.83518
 
< 0.1%
193.93200
 
0.4%
194.17183
 
0.3%
ValueCountFrequency (%)
195.63214
 
0.4%
194.17183
 
0.3%
193.93200
 
0.4%
188.83518
 
< 0.1%
183.7122
 
< 0.1%
183.04207
 
0.4%
181.25218
 
0.4%
167.8293734
7.1%
161.0594485
8.5%
153.301918
 
1.7%

Interactions

2022-10-11T12:49:18.794424image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:45.210886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:48.114731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:50.443375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:52.819267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:54.970987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:57.146333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:59.568628image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:01.767350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:03.760057image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:06.034350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:08.127334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:10.235544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:12.289912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:14.390244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:16.807506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:18.920921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:45.397946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:48.288880image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:50.573659image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:52.948730image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:55.096022image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:57.268916image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:59.700361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:01.887052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:03.880305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:06.159644image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:08.253456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:10.360828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:12.417152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:14.853978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:16.922433image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:19.047556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:45.578498image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:48.460336image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:50.716399image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:53.073961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:55.222200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:57.644542image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:59.836860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:02.008466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:04.009621image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:06.289889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:08.378288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:10.487213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:12.546701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:14.977242image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:17.042063image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:19.187852image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:45.777008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:48.649283image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:50.856655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:53.210145image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:55.365064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:57.784036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:59.992557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:02.134826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:04.143125image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:06.421434image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:08.511815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:10.616204image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:12.679210image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:15.112879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:17.174475image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:19.333822image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:45.962368image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:48.831124image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:50.989911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:53.349088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:55.502260image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:57.916288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:00.132301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:02.261129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:04.268373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:06.554832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:08.645505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:10.751883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:12.814967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:15.247467image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:17.303063image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:19.475511image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:46.142249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:48.978731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:51.339905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:53.487290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:55.641574image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:58.049113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:00.264436image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:02.391897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:04.394456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:06.692726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:08.784486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:10.889448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:12.952839image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:15.380758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:17.429040image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:19.607740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:46.287002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:49.133712image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:51.474194image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:53.625244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:55.776191image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:58.200641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:00.400160image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:02.524150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:04.523664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:06.826599image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:08.921053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:11.020818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:13.089457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:15.519831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:17.557552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:19.740970image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:46.439181image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:49.272210image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:51.607310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:53.763019image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:55.905246image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:58.333836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:00.547167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:02.651024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:04.648427image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:06.961786image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:09.051944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:11.145148image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:13.224229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:15.646548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:17.680571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:19.866181image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:46.642943image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:49.395459image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:51.733318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:53.898402image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:56.032935image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:58.458817image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:00.683309image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:02.773093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:04.768313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:07.084747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:09.184297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:11.264591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:13.347726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:15.776423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:17.798654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:19.995447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:46.810984image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:49.516476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:51.861435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:54.026709image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:56.159971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:58.582683image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:00.808114image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:02.887610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:04.880912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:07.204255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:09.307282image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:11.384236image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:13.470832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:15.898896image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:17.919985image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:20.137173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:46.973704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:49.642791image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:52.000774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:54.166816image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:56.300033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:58.712645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:00.951455image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:03.011744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:05.002147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:07.340314image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:09.444543image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:11.515847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:13.604944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:16.030370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:18.047011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:20.273448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:47.129260image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:49.784667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:52.144013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:54.302444image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:56.433722image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:58.853438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:01.109717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:03.138427image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:05.127438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:07.474744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:09.580420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:11.650100image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:13.739710image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:16.159705image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:18.175302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:20.402425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:47.357988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:49.914328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:52.276458image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:54.430465image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:56.561571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:58.987383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:01.247363image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:03.263339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:05.535755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:07.605937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:09.710711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:11.777237image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:13.871562image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:16.287070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:18.293766image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:20.538564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:47.578449image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:50.069751image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:52.409355image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:54.567016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:56.734176image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:59.158725image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:01.382311image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:03.394348image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:05.660259image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:07.740214image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:09.843889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:11.907970image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:14.004822image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:16.418506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:18.421753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:20.672363image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:47.783239image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:50.195415image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:52.545813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:54.701571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:56.875965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:59.292903image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:01.509726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:03.519548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:05.786617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:07.870625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:09.973439image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:12.032542image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:14.133049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:16.548788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:18.548213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:20.799849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:47.940731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:50.312646image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:52.677301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:54.833688image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:57.009658image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:48:59.416953image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:01.632991image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:03.632694image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:05.904024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:07.991900image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:10.097740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:12.152835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:14.255826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:16.673602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-11T12:49:18.664820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-10-11T12:49:28.479050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-11T12:49:28.696143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-11T12:49:28.895630image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-11T12:49:29.086301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-11T12:49:29.280626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-11T12:49:21.203864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-11T12:49:21.847878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexsurface_compositioncoveragesequationads_1site_1site_2ads_2site_3ads_3band_gapefermiformation_energy_per_atomtotal_magnetizationvolumeenergy_per_atomads_IE_1ads_H_1ads_S_1ads_IE_2ads_H_2ads_S_2ads_IE_3ads_H_3ads_S_3
00Ag{'CH2': 0.25}CH4(g) -H2(g) + * -> CH2*CH2bridge|A_A|A0.00.00.00.00.03.0556040.00.00847154.154777-2.83252910.3960386.39193.930.00.00.00.00.00.0
11Ag{'CH2': 0.25}CH4(g) -H2(g) + * -> CH2*CH2hollow|A_A_A|FCC0.00.00.00.00.03.0556040.00.00847154.154777-2.83252910.3960386.39193.930.00.00.00.00.00.0
22Ag{'CH2': 0.25}CH4(g) -H2(g) + * -> CH2*CH2top|A0.00.00.00.00.03.0556040.00.00847154.154777-2.83252910.3960386.39193.930.00.00.00.00.00.0
33Ag{'CH3': 0.25}CH4(g)-0.5H2(g) + * -> CH3*CH3bridge|A_A|A0.00.00.00.00.03.0556040.00.00847154.154777-2.8325299.8400145.69194.170.00.00.00.00.00.0
44Ag{'CH3': 0.25}CH4(g)-0.5H2(g) + * -> CH3*CH3hollow|A_A_A|FCC0.00.00.00.00.03.0556040.00.00847154.154777-2.8325299.8400145.69194.170.00.00.00.00.00.0
55Ag{'CH3': 0.25}CH4(g)-0.5H2(g) + * -> CH3*CH3hollow|A_A_A|HCP0.00.00.00.00.03.0556040.00.00847154.154777-2.8325299.8400145.69194.170.00.00.00.00.00.0
66Ag{'CH3': 0.25}CH4(g)-0.5H2(g) + * -> CH3*CH3top|A0.00.00.00.00.03.0556040.00.00847154.154777-2.8325299.8400145.69194.170.00.00.00.00.00.0
77Ag{'CH': 0.25}CH4(g)-1.5H2(g) + * -> CH*CHhollow|A_A_A|FCC0.00.00.00.00.03.0556040.00.00847154.154777-2.83252910.6400594.13183.040.00.00.00.00.00.0
88Ag{'CH': 0.25}CH4(g)-1.5H2(g) + * -> CH*CHhollow|A_A_A|HCP0.00.00.00.00.03.0556040.00.00847154.154777-2.83252910.6400594.13183.040.00.00.00.00.00.0
99Ag{'C': 0.25}CH4(g)-2.0H2(g) + * -> C*Chollow|A_A_A|FCC0.00.00.00.00.03.0556040.00.00847154.154777-2.83252911.2603716.68158.100.00.00.00.00.00.0

Last rows

df_indexsurface_compositioncoveragesequationads_1site_1site_2ads_2site_3ads_3band_gapefermiformation_energy_per_atomtotal_magnetizationvolumeenergy_per_atomads_IE_1ads_H_1ads_S_1ads_IE_2ads_H_2ads_S_2ads_IE_3ads_H_3ads_S_3
5278288568Zr3Co{'N': 0.25}0.5N2(g) + * -> N*Nhollow|A_A_A|FCC0.00.00.00.00.04.345283-0.2107120.000084159.739082-8.39856714.53414472.680153.3010.00.00.00.00.00.0
5278388569Zn3Co{'H': 0.25}0.5H2(g) + * -> H*Hhollow-tilt|A_A_B|FCC0.00.00.00.00.03.896215-0.0550380.42499652.052715-2.77671313.59844217.998114.7170.00.00.00.00.00.0
5278488574Bi3Zn{'OH': 0.25}H2O(g)-0.5H2(g) + * -> OH*OHhollow|A_A_A|HCP0.00.00.00.00.05.6479950.1747970.003040116.150744-3.05488113.0170038.990183.7100.00.00.00.00.00.0
5278588575Bi3Zn{'OH': 0.25}H2O(g)-0.5H2(g) + * -> OH*OHbridge-tilt|A_A|A0.00.00.00.00.05.6479950.1747970.003040116.150744-3.05488113.0170038.990183.7100.00.00.00.00.00.0
5278688576Bi3Zn{'OH': 0.25}H2O(g)-0.5H2(g) + * -> OH*OHhollow|A_A_A|FCC0.00.00.00.00.05.6479950.1747970.003040116.150744-3.05488113.0170038.990183.7100.00.00.00.00.00.0
5278788577Bi3Zn{'OH': 0.25}H2O(g)-0.5H2(g) + * -> OH*OHhollow-tilt|A_A_B|HCP0.00.00.00.00.05.6479950.1747970.003040116.150744-3.05488113.0170038.990183.7100.00.00.00.00.00.0
5278888578Bi3Zn{'OH': 0.25}H2O(g)-0.5H2(g) + * -> OH*OHtop-tilt|A0.00.00.00.00.05.6479950.1747970.003040116.150744-3.05488113.0170038.990183.7100.00.00.00.00.00.0
5278988579Bi3Zn{'OH': 0.25}H2O(g)-0.5H2(g) + * -> OH*OHtop-tilt|B0.00.00.00.00.05.6479950.1747970.003040116.150744-3.05488113.0170038.990183.7100.00.00.00.00.00.0
5279088585BiCr{'O': 0.25}H2O(g) -H2(g) + * -> O*Obridge|A_A|B0.00.00.00.00.05.2473640.6076453.750857202.654994-6.16208713.61806249.180161.0590.00.00.00.00.00.0
5279188586BiCr{'O': 0.25}H2O(g) -H2(g) + * -> O*Obridge|B_B|A0.00.00.00.00.05.2473640.6076453.750857202.654994-6.16208713.61806249.180161.0590.00.00.00.00.00.0